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This paper presents a knowledge management system for automobile failure analysis using retrieval-augmented generation (RAG) with large language models (LLMs) and knowledge graphs (KGs). In the automotive industry, there is a growing demand for knowledge transfer of failure analysis from experienced engineers to young engineers. However, failure events are phenomena that occur in a chain reaction, making them difficult for beginners to analyze them. While knowledge graphs, which can describe semantic relationships and structure information is effective in representing failure events, due to their capability of representing the relationships between components, there is much information in KGs, so it is challenging for young engineers to extract and understand sub-graphs from the KG. On the other hand, there is increasing interest in the use of Graph RAG, a type of RAG that combines LLMs and KGs for knowledge management. However, when using the current Graph RAG framework with an existing knowledge graph for automobile failures, several issues arise because it is difficult to generate executable queries for a knowledge graph database which is not constructed by LLMs. To address this, we focused on optimizing the Graph RAG pipeline for existing knowledge graphs. Using an original Q&A dataset, the ROUGE F1 score of the sentences generated by the proposed method showed an average improvement of 157.6% compared to the current method. This highlights the effectiveness of the proposed method for automobile failure analysis., Comment: 7 pages, 6 figures, to be published in 2024 IEEE International Conference on Bid Data (BigData)

Both tin monosulfide (SnS) and tin disulfide (SnS2) are thermodynamically stable layered materials with potential for spin-valleytronic devices and photodetectors. Notably, monolayer SnS, owing to its low symmetry, exhibits interesting properties such as ferroelectricity, shift-current, and a persistent spin helix state in the monolayer limit. However, creating atomic-thickness crystals of SnS is challenging owing to the enhanced interlayer interactions caused by lone pair electrons, unlike to SnS2. Here, we demonstrate that p-type SnS can be selectively grown by simply varying the sulfur vapor concentration relative to tin using single-element precursors. We show that monolayer SnS crystals, up to several tens of micrometers in lateral scale, can be easily and safely obtained by high-temperature etching of bulk SnS in a pure nitrogen gas atmosphere. These findings pave the way for device applications based on high-quality tin sulfide.

Optical neural network (ONN) has been attracting intense attention owing to their low latency and low-power consumption. Among the ONNs, optical recurrent neural network (RNN) enables low-power and high-speed time-series data processing using a compact loop structure. The loop losses need to be efficiently compensated so that the time-series information is maintained in the RNN operation. For this purpose, we focus on the optoelectronic RNN (OE-RNN) with optical-electrical-optical (OEO) converters to compensate for the loop losses. However, the effect of resistive-capacitive (RC) delay of OEO converters on the RNN performance is unclear. Here, we study in simulation an OE-RNN equipped with OEO converters with RC delay. We confirm that our modeled OE-RNN achieves the high training accuracy of time-series data classification even when RC delay is comparably large to the time interval of time-series data. Our analyses reveal that the accumulation of time-series data by RC delay does not degrade the RNN performance but rather can compensate for the degraded RNN performance due to loop losses. From the theoretical analysis referring to the gradient explosion and vanishing problems, we find the region related to loss and RC delay where the high training accuracy can be achieved. In simulation, we confirm this compensation effect in the large OE-RNN circuit up to 32$\times$32 scale. Our proposed scheme opens a new way of time-series data processing by utilizing RC delay for the optical computing and optical communication.

This paper presents an accelerated spherical K-means clustering algorithm for large-scale and high-dimensional sparse document data sets. We design an algorithm working in an architecture-friendly manner (AFM), which is a procedure of suppressing performance-degradation factors such as the numbers of instructions, branch mispredictions, and cache misses in CPUs of a modern computer system. For the AFM operation, we leverage unique universal characteristics (UCs) of a data-object and a cluster's mean set, which are skewed distributions on data relationships such as Zipf's law and a feature-value concentration phenomenon. The UCs indicate that the most part of the number of multiplications for similarity calculations is executed regarding terms with high document frequencies (df) and the most part of a similarity between an object- and a mean-feature vector is obtained by the multiplications regarding a few high mean-feature values. Our proposed algorithm applies an inverted-index data structure to a mean set, extracts the specific region with high-df terms and high mean-feature values in the mean-inverted index by newly introduced two structural parameters, and exploits the index divided into three parts for efficient pruning. The algorithm determines the two structural parameters by minimizing the approximate number of multiplications related to that of instructions, reduces the branch mispredictions by sharing the index structure including the two parameters with all the objects, and suppressing the cache misses by keeping in the caches the frequently used data in the foregoing specific region, resulting in working in the AFM. We experimentally demonstrate that our algorithm efficiently achieves superior speed performance in large-scale documents compared with algorithms using the state-of-the-art techniques., Comment: 28 pages, 23 figures

A planetary growth rate, a.k.a., the mass accretion rate, is a fundamental parameter in planet formation, as it determines a planet's final mass. Planetary mass accretion rates have been estimated using hydrogen lines, based on the models originally developed for accreting stars, known as the accretion flow model. Recently, Aoyama et al. (2018) introduced the accretion shock model as an alternative mechanism for hydrogen line emission. However, it remains unclear which model is more appropriate for accreting planets and substellar objects. To address this, we applied both models to archival data consisting of 96 data points from 76 accreting brown dwarfs and very low-mass stars, with masses ranging from approximately 0.02 to 0.1 $M_\sun$, to test which model best explains their accreting properties. The results showed that the emission mechanisms of 15 data points are best explained by the shock model, while 55 data points are best explained by the flow model. For the 15 data points explained by the planetary shock model, the shock model estimates up to several times higher mass accretion rates than the flow model. As this trend is more pronounced for planetary mass objects, it is crucial to determine which emission mechanism is dominant in individual planets. We also discuss the physical parameters that determine the emission mechanisms and the variability of line ratios., Comment: 22 pages, 12 figures, accepted in AJ

In February and March 2024, several Type I X-ray bursts from the accreting neutron star SRGA J144459.2$-$60420 were detected by multiple X-ray satellites, with the first reports coming from INTEGRAL and NinjaSat. These observations reveal that after exhibiting very regular behavior as a ``clocked" burster, the peak luminosity of the SRGA J144459.2$-$60420 X-ray bursts shows a gradual decline. The observed light curves exhibit a short plateau feature, potentially with a double peak, followed by a rapid decay in the tail-features unlike those seen in previously observed clocked bursters. In this study, we calculate a series of multizone X-ray burst models with various compositions of accreted matter, specifically varying the mass fractions of hydrogen ($X$), helium ($Y$), and heavier CNO elements or metallicity ($Z_{\rm CNO}$). We demonstrate that a model with higher $Z_{\rm CNO}$ and/or lower $X/Y$ compared to the solar values can reproduce the observed behavior of SRGA J144459.2$-$60420. Therefore, we propose that this new XRB is likely the first clocked burster with non-solar elemental compositions. Moreover, based on the X-ray burst light curve morphology in the decline phase observed by NinjaSat, a He-enhanced model with $X/Y \approx 1.5$ seems preferred over high-metallicity cases. We also give a brief discussion on the implications for the neutron star mass, binary star evolution, inclination angle, and the potential for a high-metallicity scenario, the last of which is closely related to the properties of the hot CNO cycle., Comment: 7 pages, 5 figures, submitted to PASJ Letter

The CubeSat X-ray observatory NinjaSat was launched on 2023 November 11 and has provided opportunities for agile and flexible monitoring of bright X-ray sources. On 2024 February 23, the NinjaSat team started long-term observation of the new X-ray source SRGA J144459.2$-$604207 as the first scientific target, which was discovered on 2024 February 21 and recognized as the sixth clocked X-ray burster. Our 25-day observation covered almost the entire decay of this outburst from two days after the peak at $\sim$100 mCrab on February 23 until March 18 at a few mCrab level. The Gas Multiplier Counter onboard NinjaSat successfully detected 12 Type-I X-ray bursts with a typical burst duration of $\sim$20 s, shorter than other clocked burster systems. As the persistent X-ray emission declined by a factor of five, X-ray bursts showed a notable change in its morphology: the rise time became shorter from 4.4(7) s to 0.3(3) s (1$\sigma$ errors), and the peak amplitude increased by 44%. The burst recurrence time $\Delta t_{\rm rec}$ also became longer from 2 hr to 10 hr, following the relation of $\Delta t_{\rm rec} \propto F_{\rm per}^{-0.84}$, where $F_{\rm per}$ is the persistent X-ray flux, by applying a Markov chain Monte Carlo method. The short duration of bursts is explained by the He-enhanced composition of accretion matter and the relation between $\Delta t_{\textrm{rec}}$ and $F_{\rm per}$ by a massive neutron star. This study demonstrated that CubeSat pointing observations can provide valuable astronomical X-ray data., Comment: 7 pages, 5 figures, submitted to PASJ Letter

Work on shallow discourse parsing in English has focused on the Wall Street Journal corpus, the only large-scale dataset for the language in the PDTB framework. However, the data is not openly available, is restricted to the news domain, and is by now 35 years old. In this paper, we present and evaluate a new open-access, multi-genre benchmark for PDTB-style shallow discourse parsing, based on the existing UD English GUM corpus, for which discourse relation annotations in other frameworks already exist. In a series of experiments on cross-domain relation classification, we show that while our dataset is compatible with PDTB, substantial out-of-domain degradation is observed, which can be alleviated by joint training on both datasets., Comment: Accepted to EMNLP 2024 (main, long); camera-ready version

A one-dimensional charge density wave (CDW) is driven to slide by a dc electric field, carrying an electric current. In an additional ac field with frequency ${\omega}_{\mathrm{ex}}$, it is known that the sliding CDW can be synchronized to $\omega_{\mathrm{ex}}$, leading to the occurrence of Shapiro steps in the $I$-$V$ characteristics. Motivated by a recent experiment where ac fields with two frequencies $\omega_{\mathrm{ex}}$ and $\omega_{\mathrm{ex}}^{\prime}$ are simultaneously applied, we theoretically investigate the effects of frequency mixing on the Shapiro-step formation. Based on the Fukuyama-Lee-Rice model, we show that in addition to the main steps induced by $\omega_{\mathrm{ex}}$, satellite steps characterized by $\omega_{\mathrm{ex}}^{\prime}$ emerge. It is also found that with increasing the ac-field strength for $\omega_{\mathrm{ex}}^{\prime}$, each step width first exhibits a damped oscillation as in the one-frequency case, and then, exhibits a non-monotonic behavior. The origin of these behaviors and the relevance to the associated experiment are also discussed., Comment: 6 pages, 4 figures + supplemental material

The structure of an Al(111)3x3-Si surface was examined by combining data from positron diffraction and core-level photoemission spectroscopy. Analysis of the diffraction rocking curves indicated that the overlayer had a flat honeycomb lattice structure. Simulations of Si core-level spectra calculated via the first-principles indicated that one of the Si atoms in the unit cell was replaced by an Al atom. The surface superstructure was thus a two-dimensional layer of Al-embedded silicene on Al(111).

GRAMS (Gamma-Ray and AntiMatter Survey) is a next-generation balloon/satellite experiment utilizing a LArTPC (Liquid Argon Time Projection Chamber), to simultaneously target astrophysical observations of cosmic MeV gamma-rays and conduct an indirect dark matter search using antimatter. While LArTPCs are widely used in particle physics experiments, they have never been operated at balloon altitudes. An engineering balloon flight with a small-scale LArTPC (eGRAMS) was conducted on July 27th, 2023, to establish a system for safely operating a LArTPC at balloon altitudes and to obtain cosmic-ray data from the LArTPC. The flight was launched from the Japan Aerospace Exploration Agency's (JAXA) Taiki Aerospace Research Field in Hokkaido, Japan. The total flight duration was 3 hours and 12 minutes, including a level flight of 44 minutes at a maximum altitude of 28.9 km. The flight system was landed on the sea and successfully recovered. The LArTPC was successfully operated throughout the flight, and about 0.5 million events of the cosmic-ray data including muons, protons, and Compton scattering gamma-ray candidates, were collected. This pioneering flight demonstrates the feasibility of operating a LArTPC in high-altitude environments, paving the way for future GRAMS missions and advancing our capabilities in MeV gamma-ray astronomy and dark matter research.

Accretion among planets is a poorly understood phenomenon, due to lack of both observational and theoretical studies. Detection of emission lines from accreting gas giants facilitate detailed investigations into this process. This work presents a detailed analysis of Balmer lines from one of the few known young, planetary-mass objects with observed emission, the isolated L2 dwarf 2MASS J11151597+1937266 with a mass 7-21 Mj and age 5-45 Myr, located at 45+-2 pc. We obtained the first high-resolution (R~50,000) spectrum of the target with VLT/UVES, a spectrograph in the near-UV to visible wavelengths (3200-6800 AA). We report resolved H3-H6 and He I (5875.6 AA) emission in the spectrum. Based on the asymmetric line profiles of H3 and H4, 10% width of H3 (199+-1 km/s), tentative He I 6678 AA emission and indications of a disk from MIR excess, we confirm ongoing accretion at this object. Using the Gaia update of the parallax, we revise its temperature to 1816+-63 K and radius to 1.5+-0.1 Rj. Analysis of observed H I profiles using 1D planet-surface shock model implies a pre-shock gas velocity of v0=120(+80,-40) km/s and a pre-shock density of log(n0/cm^-3)=14(+0,-5). Pre-shock velocity points to a mass of 6(+8,-4) Mj for the target. Combining the H I line luminosities and planetary Lline-Lacc scaling relations, we derive a mass accretion rate of 1.4(+2.8,-0.9)x10^-8 Mj/yr., Comment: Accepted for publication at A&A on 2024-09-11. 15 pages, 9 figures, 7 tables

This paper provides an overview, analysis, and comparison of second-order dynamic optimization algorithms, i.e., constrained Differential Dynamic Programming (DDP) and Sequential Quadratic Programming (SQP). Although a variety of these algorithms has been proposed and used successfully, there exists a gap in understanding the key differences and advantages, which we aim to provide in this work. For constrained DDP, we choose methods that incorporate nonlinear programming techniques to handle state and control constraints, including Augmented Lagrangian (AL), Interior Point, Primal Dual Augmented Lagrangian (PDAL), and Alternating Direction Method of Multipliers. Both DDP and SQP are provided in single- and multiple-shooting formulations, where constraints that arise from dynamics are encoded implicitly and explicitly, respectively. As a byproduct of the review, we also propose a single-shooting PDAL DDP which is robust to the growth of penalty parameters and performs better than the normal AL variant. We perform extensive numerical experiments on various systems with increasing complexity to investigate the quality of the solutions, the levels of constraint violation, iterations for convergence, and the sensitivity of final solutions with respect to initialization. The results show that DDP often has the advantage of finding better local minima, while SQP tends to achieve better constraint satisfaction. For multiple-shooting formulation, both DDP and SQP can enjoy informed initial guesses, while the latter appears to be more advantageous in complex systems. It is also worth highlighting that DDP provides favorable computational complexity and feedback gains as a byproduct of optimization., Comment: 31 pages, 11 figures

We present a novel second-order trajectory optimization algorithm based on Stein Variational Newton's Method and Maximum Entropy Differential Dynamic Programming. The proposed algorithm, called Stein Variational Differential Dynamic Programming, is a kernel-based extension of Maximum Entropy Differential Dynamic Programming that combines the best of the two worlds of sampling-based and gradient-based optimization. The resulting algorithm avoids known drawbacks of gradient-based dynamic optimization in terms of getting stuck to local minima, while it overcomes limitations of sampling-based stochastic optimization in terms of introducing undesirable stochasticity when applied in online fashion. To test the efficacy of the proposed algorithm, experiments are performed for both trajectory optimization and model predictive control. The experiments include comparisons with unimodal and multimodal Maximum Entropy Differential Dynamic Programming as well as Model Predictive Path Integral Control and its multimodal and Stein Variational extensions. The results demonstrate the superior performance of the proposed algorithms and confirm the hypothesis that there is a middle ground between sampling and gradient-based optimization that is indeed beneficial for the purposes of dynamic optimization. This middle ground consists of different mechanisms that combine sampling with gradient-based optimization. In this paper, we investigate these different mechanisms and show their benefits in dealing with non-convex dynamic optimization problems found in trajectory optimization and model predictive control., Comment: 30 pages, 10 figures

This paper is devoted to the problem of finding a common fixed point of quasinonexpansive mappings defined on a Hilbert space. To approximate the solution to this problem, we present several iterative processes using the parallel method based on Anh and Chung (2014) and Aoyama (2018).

Hydrogen lines from forming planets are crucial for understanding planet formation. However, the number of planetary hydrogen line detections is still limited. Recent JWST/NIRSpec observations have detected Paschen and Brackett hydrogen lines at TWA 27 B (2M1207b). TWA 27 B is classified as a planetary-mass companison (PMC) rather than a planet due to its large mass ratio to the central object ($\approx 5 M_\mathrm{J}$ compared to $25 M_\mathrm{J}$). Nevertheless, TWA 27 B's hydrogen line emission is expected to be same as for planets, given its small mass. We aim to constrain the accretion properties and accretion geometry of TWA 27 B, contributing to our understanding of hydrogen line emission mechanism common to both PMCs and planets. We conduct spectral fitting of four bright hydrogen lines (Pa-$\alpha$, Pa-$\beta$, Pa-$\gamma$, Pa-$\delta$) with an accretion-shock emission model tailored for forming planets. We estimate the mass accretion rate at $\dot{M} \approx 3 \times 10^{-9}\, M_\mathrm{J}\,\mathrm{yr}^{-1}$ with our fiducial parameters, though this is subject to an uncertainty of up to factor of ten. Our analysis also indicates a dense accretion flow, $n\gtrsim 10^{13}\,\mathrm{cm^{-3}}$ just before the shock, implying a small accretion-shock filling factor $f_\mathrm{f}$ on the planetary surface ($f_\mathrm{f} \lesssim 5\times10^{-4}$). This finding suggests that magnetospheric accretion is occurring at TWA 27 B. Additionally, we carry out a comparative analysis of hydrogen-line emission color to identify the emission mechanism, but the associated uncertainties proved too large for definitive conclusions. This underscores the need for further high-precision observational studies to elucidate these emission mechanisms fully., Comment: 14 pages, 6 figures, Accepted for publication in the Astronomical Journal

We deform the heat kernel and the Brownian motion on $\mathbb{R}^{N}$ from the perspective of "$(k,a)$-generalized Fourier analysis" with $k=0$. This is a new type of harmonic analysis proposed by S.Ben Sa\"id--T.Kobayashi--B.{\O}rsted from the representation theoretic viewpoint. In this paper, we construct the $a$-deformed heat kernel and $a$-deformed Brownian motion, and explore their some basic properties. We also prove that the $(k,a)$-generalized Fourier integral kernels are polynomial growth when $k=0$, for a justification of some discussions., Comment: 58 pages

We report scale setting and hadronic properties for our new lattice QCD gauge configuration set (HAL-conf-2023). We employ $(2+1)$-flavor nonperturbatively improved Wilson fermions with stout smearing and the Iwasaki gauge action on a $96^4$ lattice, and generate configurations of 8,000 trajectories at the physical point. We show the basic properties of the configurations such as the plaquette value, topological charge distribution and their auto-correlation times. The scale setting is performed by detailed analyses of the $\Omega$ baryon mass. We calculate the physical results of quark masses, decay constants of pseudoscalar mesons and single hadron spectra in light quark sector. The masses of the stable hadrons are found to agree with the experimental values within a sub-percent level., Comment: 32 pages and 18 figures; v2: reference(s) added; v3: accepted for publication in Phys. Rev. D; v4: typo(s) corrected, published version

Large Language Models (LLMs) are gaining popularity in the field of robotics. However, LLM-based robots are limited to simple, repetitive motions due to the poor integration between language models, robots, and the environment. This paper proposes a novel approach to enhance the performance of LLM-based autonomous manipulation through Human-Robot Collaboration (HRC). The approach involves using a prompted GPT-4 language model to decompose high-level language commands into sequences of motions that can be executed by the robot. The system also employs a YOLO-based perception algorithm, providing visual cues to the LLM, which aids in planning feasible motions within the specific environment. Additionally, an HRC method is proposed by combining teleoperation and Dynamic Movement Primitives (DMP), allowing the LLM-based robot to learn from human guidance. Real-world experiments have been conducted using the Toyota Human Support Robot for manipulation tasks. The outcomes indicate that tasks requiring complex trajectory planning and reasoning over environments can be efficiently accomplished through the incorporation of human demonstrations., Comment: IEEE Robotics and Automation Letters

We consider a machine learning setup where one training dataset is used to train multiple models across slightly different data distributions. This occurs when customized models are needed for various deployment environments. To reduce storage and training costs, we propose the DRSSS method, which combines distributionally robust (DR) optimization and safe sample screening (SSS). The key benefit of this method is that models trained on the reduced dataset will perform the same as those trained on the full dataset for all possible different environments. In this paper, we focus on covariate shift as a type of data distribution change and demonstrate the effectiveness of our method through experiments.

In this study, we propose a method Distributionally Robust Safe Screening (DRSS), for identifying unnecessary samples and features within a DR covariate shift setting. This method effectively combines DR learning, a paradigm aimed at enhancing model robustness against variations in data distribution, with safe screening (SS), a sparse optimization technique designed to identify irrelevant samples and features prior to model training. The core concept of the DRSS method involves reformulating the DR covariate-shift problem as a weighted empirical risk minimization problem, where the weights are subject to uncertainty within a predetermined range. By extending the SS technique to accommodate this weight uncertainty, the DRSS method is capable of reliably identifying unnecessary samples and features under any future distribution within a specified range. We provide a theoretical guarantee of the DRSS method and validate its performance through numerical experiments on both synthetic and real-world datasets.

Natural language generation tools are powerful and effective for generating content. However, language models are known to display bias and fairness issues, making them impractical to deploy for many use cases. We here focus on how fairness issues impact automatically generated test content, which can have stringent requirements to ensure the test measures only what it was intended to measure. Specifically, we review test content generated for a large-scale standardized English proficiency test with the goal of identifying content that only pertains to a certain subset of the test population as well as content that has the potential to be upsetting or distracting to some test takers. Issues like these could inadvertently impact a test taker's score and thus should be avoided. This kind of content does not reflect the more commonly-acknowledged biases, making it challenging even for modern models that contain safeguards. We build a dataset of 601 generated texts annotated for fairness and explore a variety of methods for classification: fine-tuning, topic-based classification, and prompting, including few-shot and self-correcting prompts. We find that combining prompt self-correction and few-shot learning performs best, yielding an F1 score of 0.79 on our held-out test set, while much smaller BERT- and topic-based models have competitive performance on out-of-domain data., Comment: 19 pages, 4 figures, accepted to the 19th Workshop on Innovative Use of NLP for Building Educational Applications

We present a sampling-based trajectory optimization method derived from the maximum entropy formulation of Differential Dynamic Programming with Tsallis entropy. This method is a generalization of the legacy work with Shannon entropy, which leads to a Gaussian optimal control policy for exploration during optimization. With the Tsallis entropy, the policy takes the form of $q$-Gaussian, which further encourages exploration with its heavy-tailed shape. Moreover, the sampling variance is scaled according to the value function of the trajectory. This scaling mechanism is the unique property of the algorithm with Tsallis entropy in contrast to the original formulation with Shannon entropy, which scales variance with a fixed temperature parameter. Due to this property, our proposed algorithms can promote exploration when necessary, that is, the cost of the trajectory is high. The simulation results with two robotic systems with multimodal cost demonstrate the properties of the proposed algorithm., Comment: 7 pages, 5 figures, This paper is for CDC 2024

In this article we present Enhanced Rhetorical Structure Theory (eRST), a new theoretical framework for computational discourse analysis, based on an expansion of Rhetorical Structure Theory (RST). The framework encompasses discourse relation graphs with tree-breaking, non-projective and concurrent relations, as well as implicit and explicit signals which give explainable rationales to our analyses. We survey shortcomings of RST and other existing frameworks, such as Segmented Discourse Representation Theory (SDRT), the Penn Discourse Treebank (PDTB) and Discourse Dependencies, and address these using constructs in the proposed theory. We provide annotation, search and visualization tools for data, and present and evaluate a freely available corpus of English annotated according to our framework, encompassing 12 spoken and written genres with over 200K tokens. Finally, we discuss automatic parsing, evaluation metrics and applications for data in our framework.

TWA 27B (2M1207b) is the first directly-imaged planetary-mass (MP ~ 5 MJ) companion (Chauvin et al. 2004) and was observed at 0.9--5.3 micron with JWST/NIRSpec (Luhman et al. 2023). To understand the accretion properties of TWA 27B, we search for continuum-subtracted near-infrared helium and hydrogen emission lines and measure their widths and luminosities. We detect the He I triplet at 4.3 sigma and all Paschen-series lines covered by NIRSpec (Pa alpha, Pa beta, Pa gamma, Pa delta) at 4--5 sigma. The three brightest Brackett-series lines (Br alpha, Br beta, Br gamma) as well as Pf gamma and Pf delta are tentative detections at 2--3 sigma. We provide upper limits on the other hydrogen lines, including on H alpha through Hubble Space Telescope archival data. Three lines can be reliably deconvolved to reveal an intrinsic width Delta v = 67+-9 km/s, which is 60% of the surface freefall velocity. The line luminosities seem significantly too high to be due to chromospheric activity. Converting line luminosities to an accretion rate yields Mdot ~ 5e-9 MJ/yr when using scaling relationships for planetary masses, and Mdot ~ 0.1e-9 MJ/yr with extrapolated stellar scalings. Several of these lines represent first detections at an accretor of such low mass. The weak accretion rate implies that formation is likely over. This analysis shows that JWST can be used to measure low line-emitting mass accretion rates onto planetary-mass objects, motivates deeper searches for the mass reservoir feeding TWA 27B, and hints that other young directly-imaged objects might -- hitherto unbeknownst -- also be accreting., Comment: In press at ApJ. Main text: 10 pages, 5 figures, 1 table, 1 equation. Appendix: 12 figures (line profiles). Comments welcome. v2: added one reference. v3: added RV velocity (probable systemic velocity) of primary to Fig. 3b. v4: no change, only added DOI of open-access, published version (2024 March 18)

Recent high-sensitivity observations reveal that accreting giant planets embedded in their parental circumstellar disks can emit H$\alpha$ at their final formation stages. While the origin of such emission is not determined yet, magnetospheric accretion is currently a most plausible hypothesis. In order to test this hypothesis further, we develop a simplified, but physical-based model and apply it to our observations taken toward HD 163296 with Subaru/SCExAO+VAMPIRES. We specify under what conditions, embedded giant planets can undergo magnetospheric accretion and emit hydrogen lines. We find that when stellar accretion rates are high, magnetospheric accretion becomes energetic enough to self-regulate the resulting emission. On the other hand, if massive planets are embedded in disks with low accretion rates, earlier formation histories determine whether magnetospheric accretion occurs. We explore two different origins of hydrogen emission lines (magnetospheric accretion flow heated by accretion-related processes vs planetary surfaces via accretion shock). The corresponding relationships between the accretion and line luminosities dictate that emission from accretion flow achieves higher line flux than that from accretion shock and the flux decreases with increasing wavelengths (i.e., from H$\alpha$ to Pa$\beta$ and up to Br$\gamma$). Our observations do not detect any point-like source emitting H$\alpha$ and are used to derive the 5$\sigma$ detection limit. The observations are therefore not sensitive enough, and reliable examination of our model becomes possible if observational sensitivity will be improved by a factor of ten or more. Multi-band observations increase the possibility of efficiently detecting embedded giant planets and carefully determining the origin of hydrogen emission lines., Comment: 21 pages, 9 figures, 3 tables; accepted for publication in AJ